{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-carson-katri--dream-textures","slug":"carson-katri--dream-textures","name":"dream-textures","type":"repo","url":"https://github.com/carson-katri/dream-textures","page_url":"https://unfragile.ai/carson-katri--dream-textures","categories":["image-generation"],"tags":["ai","blender","blender-addon","image-generation","stable-diffusion"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-carson-katri--dream-textures__cap_0","uri":"capability://image.visual.text.to.image.texture.generation.with.stable.diffusion","name":"text-to-image texture generation with stable diffusion","description":"Generates 2D textures from natural language prompts by integrating Hugging Face Diffusers pipeline directly into Blender's UI layer. The DreamTexture operator collects prompt parameters (text, negative prompt, seed, guidance scale, steps) from a DreamPrompt property group, launches a background generator process to avoid blocking Blender's UI, and pipes the diffusers output directly into Blender's image editor. Supports multi-platform GPU acceleration (CUDA, DirectML, MPS, ROCm) with automatic device selection and fallback to CPU.","intents":["Generate photorealistic or stylized textures from text descriptions without leaving Blender","Rapidly iterate on texture concepts by tweaking prompts and re-generating in real-time","Create base textures for 3D models using AI without external tools or API calls"],"best_for":["3D artists and game developers using Blender as primary workflow","texture designers wanting local, offline generation without cloud dependencies","teams needing reproducible texture generation with seed control"],"limitations":["Generation speed depends on GPU VRAM; 4GB minimum but 8GB+ recommended for quality","Background process adds ~500ms-5s latency per generation depending on step count and hardware","Blender UI remains responsive but generation cannot be parallelized across multiple prompts","Output resolution limited by available VRAM; typical max 768x768 on 4GB, 1024x1024 on 8GB+"],"requires":["Blender 3.0+","Python 3.9+","4GB+ GPU VRAM (NVIDIA CUDA, Apple Silicon MPS, AMD ROCm, or Intel DirectML)","5GB+ disk space for model weights"],"input_types":["text (prompt string)","text (negative prompt)","integer (seed, steps, guidance scale, width, height)"],"output_types":["image (PNG/EXR in Blender image editor)","texture (directly assignable to Blender materials)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_1","uri":"capability://image.visual.image.to.image.texture.refinement.with.strength.control","name":"image-to-image texture refinement with strength control","description":"Modifies existing textures or images by passing them through the Stable Diffusion img2img pipeline with configurable denoising strength. The operator accepts an input image from Blender's image editor, applies the diffusers img2img pipeline with user-defined strength (0-1 scale controlling how much the original is preserved), and outputs a refined texture. Supports negative prompts and all generation parameters (seed, steps, guidance) to enable fine-grained control over stylization vs. preservation.","intents":["Refine or re-style existing textures without starting from scratch","Apply artistic filters or material changes to photographic base textures","Iteratively improve texture quality by adjusting strength and re-running generation"],"best_for":["texture artists refining existing assets","game developers adapting textures to match art direction","VFX artists applying style transfers to animation frames"],"limitations":["Strength parameter is non-linear; values <0.3 preserve original heavily, >0.8 approach text-to-image behavior","Artifacts may appear at edges if input image resolution doesn't match generation resolution","Cannot selectively modify regions without using inpainting mode separately"],"requires":["Blender 3.0+","Existing image loaded in Blender image editor","4GB+ GPU VRAM","Stable Diffusion model weights"],"input_types":["image (PNG, EXR, or other Blender-supported format)","text (prompt and negative prompt)","float (strength 0.0-1.0, seed, guidance scale)"],"output_types":["image (refined texture in Blender image editor)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_10","uri":"capability://image.visual.animation.re.styling.with.custom.render.pass.integration","name":"animation re-styling with custom render pass integration","description":"Applies AI-generated textures to animation frames by integrating with Blender's render engine and custom render passes. The operator renders animation frames with a custom pass (e.g., diffuse color, normal map), passes each frame through the img2img pipeline with a consistent prompt and seed offset, and outputs a re-styled animation. Maintains temporal coherence by using frame-based seed offsets and optical flow guidance to minimize flickering between frames.","intents":["Apply consistent AI-generated style to animation sequences without manual frame-by-frame editing","Re-style animated textures or materials across multiple frames in a single batch operation","Create stylized animations by applying img2img to render passes with temporal consistency"],"best_for":["VFX artists applying AI-generated effects to animation sequences","game developers re-styling animated materials or textures","motion graphics artists applying consistent AI filters to video"],"limitations":["Temporal coherence is approximate; some flickering may occur between frames despite seed offset strategy","Batch processing is sequential; rendering and processing 100+ frames is time-consuming (hours on typical hardware)","Custom render passes must be pre-configured in Blender; incorrect pass setup produces poor results","Memory usage is high for long sequences; intermediate frames must be stored or streamed to disk"],"requires":["Blender 3.0+ with Cycles or Eevee render engine","Animation sequence (pre-rendered or live render)","Custom render pass configured (diffuse, normal, etc.)","4GB+ GPU VRAM per frame","Disk space for intermediate frame storage"],"input_types":["animation frames (PNG, EXR sequence)","custom render pass (diffuse, normal, or other)","text (prompt and negative prompt)","float (seed base, guidance scale, img2img strength)"],"output_types":["animation frames (re-styled PNG or EXR sequence)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_11","uri":"capability://image.visual.node.based.texture.generation.with.custom.render.engine","name":"node-based texture generation with custom render engine","description":"Enables procedural texture generation workflows by implementing a custom Blender render engine that integrates Stable Diffusion into the Shader Editor node system. Artists can create node graphs with DreamTexture nodes (text-to-image, img2img, upscale, etc.), connect them to material outputs, and render to generate textures procedurally. Supports node inputs for prompts, parameters, and conditioning images, enabling complex multi-stage generation pipelines.","intents":["Build complex texture generation workflows using Blender's node-based interface","Chain multiple AI operations (text-to-image → upscale → inpaint) in a single node graph","Create reusable texture generation templates by saving node graphs as Blender files"],"best_for":["technical artists comfortable with node-based workflows","studios building reusable texture generation pipelines","developers extending Dream Textures with custom nodes"],"limitations":["Node-based interface adds complexity; steep learning curve for non-technical artists","Node graph evaluation is sequential; cannot parallelize independent branches","Debugging node graphs is difficult; errors in one node propagate downstream without clear error messages","Custom render engine integration adds ~10-20% overhead compared to direct operator calls"],"requires":["Blender 3.0+ with Shader Editor","Custom render engine registered with Blender","Understanding of Blender's node system and material properties"],"input_types":["node graph (DreamTexture nodes connected in Shader Editor)","material properties (connected to node inputs)"],"output_types":["texture (rendered from node graph)","material (with generated texture assigned)"],"categories":["image-visual","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_12","uri":"capability://memory.knowledge.model.management.with.automatic.downloading.and.caching","name":"model management with automatic downloading and caching","description":"Manages Stable Diffusion model weights by automatically downloading, caching, and versioning models from Hugging Face. The operator queries available models, downloads selected models on first use, caches them locally to avoid re-downloading, and manages disk space by allowing users to delete unused models. Supports multiple model variants (base, inpainting, upscaling, ControlNet) with independent caching.","intents":["Automatically download and cache Stable Diffusion models without manual setup","Switch between model variants (base, inpainting, upscaling) without re-downloading","Manage disk space by selectively deleting unused models"],"best_for":["users wanting plug-and-play model management without manual downloads","teams with limited disk space needing selective model caching","developers building on Dream Textures who need programmatic model access"],"limitations":["Initial model download is slow (5-30 minutes depending on internet speed and model size)","Models are cached to disk; total cache size can exceed 50GB for all variants","No automatic model updates; users must manually delete and re-download to get newer versions","Hugging Face API rate limits may cause download failures if multiple users download simultaneously"],"requires":["Blender 3.0+","Internet connection for initial model download","10-50GB free disk space depending on models used","Hugging Face account (optional, for private models)"],"input_types":["model identifier (string, e.g., 'runwayml/stable-diffusion-v1-5')","model variant (string, e.g., 'inpainting', 'upscaling')"],"output_types":["model weights (cached locally)","model metadata (size, download status, cache location)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_13","uri":"capability://tool.use.integration.performance.optimization.with.memory.efficient.inference","name":"performance optimization with memory-efficient inference","description":"Optimizes generation speed and memory usage through multiple techniques: mixed-precision inference (float16 on GPU), attention slicing to reduce peak memory, model quantization, and VAE tiling for high-resolution outputs. The operator in `optimizations.py` applies these techniques based on available VRAM, enabling generation on lower-end GPUs (4GB) that would otherwise fail. Supports progressive optimization levels (aggressive, balanced, quality) for user control.","intents":["Enable texture generation on GPUs with limited VRAM (4GB) without quality loss","Accelerate generation speed by 20-50% through mixed-precision and attention optimization","Generate high-resolution textures (1024x1024+) on mid-range GPUs"],"best_for":["users with older or lower-end GPUs (4-6GB VRAM) wanting acceptable generation speed","production pipelines needing consistent performance across heterogeneous hardware","developers optimizing Dream Textures for resource-constrained environments"],"limitations":["Mixed-precision inference may produce slightly different results than full float32; not suitable for pixel-perfect reproducibility","Attention slicing reduces speed by ~10-20% compared to full attention; trade-off between memory and speed","Quantization can reduce output quality if applied too aggressively; requires tuning per model","VAE tiling introduces seams at tile boundaries; post-processing may be needed for seamless output"],"requires":["Blender 3.0+ with Python 3.9+","GPU with float16 support (most modern GPUs)","4GB+ VRAM (minimum; 8GB+ recommended for best results)"],"input_types":["optimization level (string: 'aggressive', 'balanced', 'quality')","available VRAM (integer, auto-detected)"],"output_types":["optimized pipeline configuration (mixed precision, attention slicing, quantization flags)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_2","uri":"capability://image.visual.depth.aware.texture.generation.with.depth.to.image","name":"depth-aware texture generation with depth-to-image","description":"Generates textures that respect 3D geometry by using depth maps as conditioning input to the Stable Diffusion pipeline. The operator extracts or accepts a depth map (from Blender's depth render pass or external source), passes it alongside the text prompt to the diffusers DepthToImagePipeline, and produces a texture that aligns with the geometric structure. Enables AI-generated textures to follow surface contours and relief patterns.","intents":["Generate textures that naturally follow 3D surface geometry and depth variations","Create relief textures (normal maps, displacement) that match existing model topology","Apply AI-generated materials to complex geometry without manual UV unwrapping or projection"],"best_for":["3D modelers generating textures for high-poly or complex geometry","game developers creating displacement-mapped surfaces","architectural visualization artists applying materials to detailed models"],"limitations":["Depth map quality directly impacts output; low-resolution or noisy depth causes artifacts","Requires pre-rendered depth pass from Blender, adding one extra render step per generation","DepthToImagePipeline is less stable than text-to-image; may produce inconsistent results with extreme depth ranges","Output resolution must match depth map resolution; mismatches cause warping"],"requires":["Blender 3.0+","Depth map (from Blender render pass or imported externally)","4GB+ GPU VRAM","Stable Diffusion model with depth conditioning support"],"input_types":["image (depth map, grayscale or single-channel)","text (prompt and negative prompt)","float (seed, guidance scale, steps)"],"output_types":["image (depth-conditioned texture)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_3","uri":"capability://image.visual.inpainting.and.outpainting.with.mask.based.editing","name":"inpainting and outpainting with mask-based editing","description":"Enables selective texture modification by accepting a mask image that defines which regions to regenerate. The operator loads a mask (white = regenerate, black = preserve) alongside the base image and prompt, passes both to the diffusers inpainting pipeline, and outputs a texture with only masked regions modified. Supports outpainting (extending textures beyond original boundaries) by expanding the canvas and masking the new regions.","intents":["Fix or replace specific regions of a texture without affecting surrounding areas","Extend textures beyond original boundaries (outpainting) for seamless tiling","Selectively apply style changes or material updates to portions of a texture"],"best_for":["texture artists performing surgical edits on existing assets","game developers extending textures for larger surfaces","VFX artists removing or replacing unwanted texture elements"],"limitations":["Mask quality is critical; soft edges or anti-aliasing cause visible seams in output","Outpainting quality degrades at edges; seamless tiling requires additional post-processing","Inpainting pipeline is slower than text-to-image due to mask processing overhead (~1.5x latency)","Blending between masked and unmasked regions may show visible artifacts if guidance scale is too high"],"requires":["Blender 3.0+","Base image and corresponding mask image (same resolution)","4GB+ GPU VRAM","Stable Diffusion inpainting model variant"],"input_types":["image (base texture)","image (mask, binary or grayscale)","text (prompt and negative prompt)","float (seed, guidance scale, steps, inpaint strength)"],"output_types":["image (inpainted or outpainted texture)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_4","uri":"capability://image.visual.seamless.tileable.texture.generation.with.periodic.boundary.conditions","name":"seamless/tileable texture generation with periodic boundary conditions","description":"Generates textures that tile seamlessly by applying periodic boundary conditions during generation. The operator enables 'seamless mode' in the DreamPrompt configuration, which modifies the diffusers pipeline to wrap gradients at image edges, ensuring left/right and top/bottom edges match perfectly. Produces textures that repeat without visible seams when tiled in 2D or applied to UV-mapped surfaces.","intents":["Generate repeating textures for large surfaces without visible tiling artifacts","Create seamless materials for game environments and architectural visualization","Produce tileable textures that can be applied at any scale without pattern repetition"],"best_for":["game developers creating repeating surface materials","architectural visualization artists applying seamless textures to large surfaces","texture artists generating tileable assets for production pipelines"],"limitations":["Seamless mode reduces generation quality slightly due to edge-wrapping constraints; may produce less detailed or coherent patterns","Only works for square or rectangular textures; non-uniform aspect ratios may produce visible discontinuities","Seamless textures are limited to ~512x512 resolution before quality degradation becomes noticeable","Cannot guarantee seamlessness with all prompts; some patterns (e.g., directional or perspective-based) resist tiling"],"requires":["Blender 3.0+","Seamless mode enabled in DreamPrompt configuration","4GB+ GPU VRAM","Stable Diffusion model with periodic boundary condition support"],"input_types":["text (prompt and negative prompt)","integer (seed, steps, guidance scale, square resolution)"],"output_types":["image (seamless/tileable texture)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_5","uri":"capability://image.visual.controlnet.based.conditional.texture.generation","name":"controlnet-based conditional texture generation","description":"Enables fine-grained control over texture generation by conditioning the Stable Diffusion pipeline on additional inputs (edge maps, pose skeletons, semantic segmentation, etc.). The operator accepts a ControlNet model identifier and conditioning image, integrates it into the diffusers pipeline via the ControlNetModel class, and generates textures that respect the conditioning structure while following the text prompt. Supports multiple ControlNet models stacked for complex constraints.","intents":["Generate textures that follow specific edge structures or line art sketches","Create textures constrained by semantic segmentation maps or material boundaries","Apply pose or structure conditioning to ensure generated textures align with 3D geometry"],"best_for":["texture artists wanting precise control over generation structure","game developers applying textures to pre-defined material regions","3D artists ensuring generated textures respect geometric constraints"],"limitations":["ControlNet adds ~30-50% latency overhead per model due to additional forward passes","Conditioning image quality directly impacts output; low-resolution or noisy conditioning causes weak control","Stacking multiple ControlNets can cause conflicting constraints; requires careful prompt engineering","Not all ControlNet models are compatible with all Stable Diffusion versions; version mismatches cause errors"],"requires":["Blender 3.0+","ControlNet model weights (downloaded separately from Hugging Face)","Conditioning image (edge map, pose skeleton, segmentation, etc.)","6GB+ GPU VRAM for single ControlNet; 8GB+ for multiple stacked models","Stable Diffusion base model compatible with ControlNet"],"input_types":["image (conditioning image: edge map, pose, segmentation, etc.)","text (prompt and negative prompt)","float (seed, guidance scale, steps, controlnet conditioning scale 0.0-1.0)","string (ControlNet model identifier)"],"output_types":["image (ControlNet-conditioned texture)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_6","uri":"capability://image.visual.texture.projection.onto.3d.models.with.uv.mapping","name":"texture projection onto 3d models with uv mapping","description":"Applies AI-generated 2D textures to 3D models by leveraging Blender's UV mapping system. The operator takes a generated texture, applies it to the selected model's material via Blender's Shader Editor, and optionally projects it onto the model's surface using UV coordinates. Supports both direct material assignment and procedural projection for complex geometries.","intents":["Directly apply generated textures to 3D models without manual UV unwrapping or material setup","Project textures onto complex geometry that lacks proper UVs","Rapidly iterate on material appearance by regenerating and re-projecting textures"],"best_for":["3D modelers and game developers applying textures to models in real-time","VFX artists projecting AI textures onto complex geometry","game developers prototyping material variations quickly"],"limitations":["Projection quality depends on existing UV layout; poorly unwrapped models show distortion","Complex geometries may require manual UV adjustment before projection works correctly","Texture resolution must be chosen carefully to avoid memory overhead when applied to high-poly models","Material assignment is non-destructive but requires Blender's Shader Editor knowledge to customize further"],"requires":["Blender 3.0+","3D model with valid geometry and (ideally) pre-existing UV map","Generated texture image in Blender image editor","Model selected in Blender viewport"],"input_types":["image (generated texture)","3D model (Blender mesh object)"],"output_types":["material (Blender material with texture assigned)","3D model (with texture applied to surface)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_7","uri":"capability://image.visual.upscaling.with.super.resolution.models","name":"upscaling with super-resolution models","description":"Increases texture resolution using integrated super-resolution models (e.g., Real-ESRGAN, BSRGAN). The operator accepts a low-resolution texture, applies a selected upscaling model via the diffusers upscaler pipeline, and outputs a higher-resolution version with detail enhancement. Supports multiple upscaling factors (2x, 4x, 8x) and model variants optimized for different content types (faces, general textures, anime).","intents":["Upscale low-resolution generated textures to production-quality resolution","Enhance texture detail without regenerating at high resolution (faster than text-to-image at 1024x1024)","Apply super-resolution to existing textures for quality improvement"],"best_for":["texture artists needing production-quality resolution without long generation times","game developers upscaling textures for high-end platforms","VFX artists enhancing texture detail for close-up shots"],"limitations":["Upscaling adds ~1-3s latency per 2x factor depending on model and resolution","Super-resolution can introduce artifacts (hallucination, over-sharpening) if source texture is very low quality","Upscaling models are optimized for specific content types; wrong model choice produces suboptimal results","Memory usage scales with output resolution; 4x upscaling of 512x512 requires significant VRAM"],"requires":["Blender 3.0+","Low-resolution texture image in Blender image editor","4GB+ GPU VRAM (8GB+ recommended for 4x upscaling)","Super-resolution model weights (Real-ESRGAN, BSRGAN, or similar)"],"input_types":["image (low-resolution texture)","integer (upscaling factor: 2, 4, or 8)","string (upscaling model variant)"],"output_types":["image (upscaled high-resolution texture)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_8","uri":"capability://memory.knowledge.generation.history.tracking.and.undo.redo.with.dreamprompt.snapshots","name":"generation history tracking and undo/redo with dreamprompt snapshots","description":"Maintains a complete history of texture generations by storing DreamPrompt property snapshots (prompt, seed, parameters, timestamp) alongside generated images. The operator automatically logs each generation to a history database, enabling artists to revisit previous generations, modify parameters incrementally, and undo/redo operations without losing generation metadata. Supports filtering and searching history by prompt keywords or parameter ranges.","intents":["Revisit and iterate on previous texture generations without losing parameter information","Compare multiple generations with slightly different parameters to find optimal settings","Maintain audit trail of texture generation for reproducibility and collaboration"],"best_for":["texture artists iterating on designs and needing to backtrack to previous attempts","teams collaborating on textures and needing reproducible generation parameters","production pipelines requiring audit trails of asset generation"],"limitations":["History database grows with each generation; old entries must be manually pruned to avoid disk bloat","History is local to Blender project; not synchronized across team members or machines","Searching/filtering history is linear; large histories (1000+ entries) may be slow to query","History snapshots do not include model weights or ControlNet versions; regenerating old prompts may produce different results if models are updated"],"requires":["Blender 3.0+","Local storage for history database (SQLite or JSON file)","Disk space proportional to number of generations (each entry ~1KB metadata)"],"input_types":["DreamPrompt property group (automatically captured per generation)"],"output_types":["history entry (prompt, seed, parameters, timestamp, image reference)","filtered history list (searchable by prompt or parameters)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-carson-katri--dream-textures__cap_9","uri":"capability://tool.use.integration.multi.platform.gpu.acceleration.with.automatic.device.selection","name":"multi-platform gpu acceleration with automatic device selection","description":"Automatically detects and selects the optimal compute device (NVIDIA CUDA, Apple Silicon MPS, AMD ROCm, Intel DirectML, or CPU fallback) based on available hardware. The operator in `choose_device.py` queries system capabilities, loads platform-specific optimizations (DirectML patches for Windows, MPS kernels for macOS), and configures the diffusers pipeline to use the selected device. Supports mixed-precision inference (float16 on GPU, float32 fallback) to optimize memory usage.","intents":["Enable texture generation on diverse hardware without manual device configuration","Optimize generation speed by automatically selecting the fastest available compute device","Support users on Windows, macOS, and Linux with platform-specific optimizations"],"best_for":["users on diverse hardware platforms (Windows, macOS, Linux) wanting plug-and-play setup","teams with heterogeneous hardware needing consistent generation performance","developers building on top of Dream Textures who need cross-platform compatibility"],"limitations":["Device selection is automatic; users cannot manually override device choice without code modification","Mixed-precision inference may produce slightly different results than full float32; not suitable for pixel-perfect reproducibility","CPU fallback is very slow (10-100x slower than GPU); not practical for interactive use","Some older GPUs or drivers may not support the selected device; fallback to CPU without clear error messaging"],"requires":["Blender 3.0+ with Python 3.9+","GPU drivers (NVIDIA CUDA Toolkit, Apple Metal, AMD ROCm, or Intel DirectML)","Platform-specific dependencies (CUDA Toolkit for NVIDIA, Xcode for macOS, etc.)"],"input_types":["system hardware configuration (detected automatically)"],"output_types":["device selection (torch.device object)","optimization configuration (mixed precision, memory optimization flags)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Blender 3.0+","Python 3.9+","4GB+ GPU VRAM (NVIDIA CUDA, Apple Silicon MPS, AMD ROCm, or Intel DirectML)","5GB+ disk space for model weights","Existing image loaded in Blender image editor","4GB+ GPU VRAM","Stable Diffusion model weights","Blender 3.0+ with Cycles or Eevee render engine","Animation sequence (pre-rendered or live render)","Custom render pass configured (diffuse, normal, etc.)"],"failure_modes":["Generation speed depends on GPU VRAM; 4GB minimum but 8GB+ recommended for quality","Background process adds ~500ms-5s latency per generation depending on step count and hardware","Blender UI remains responsive but generation cannot be parallelized across multiple prompts","Output resolution limited by available VRAM; typical max 768x768 on 4GB, 1024x1024 on 8GB+","Strength parameter is non-linear; values <0.3 preserve original heavily, >0.8 approach text-to-image behavior","Artifacts may appear at edges if input image resolution doesn't match generation resolution","Cannot selectively modify regions without using inpainting mode separately","Temporal coherence is approximate; some flickering may occur between frames despite seed offset strategy","Batch processing is sequential; rendering and processing 100+ frames is time-consuming (hours on typical hardware)","Custom render passes must be pre-configured in Blender; incorrect pass setup produces poor results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6193205145049986,"quality":0.35,"ecosystem":0.55,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:42.318Z","last_commit":"2024-08-26T02:23:11Z"},"community":{"stars":8157,"forks":435,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=carson-katri--dream-textures","compare_url":"https://unfragile.ai/compare?artifact=carson-katri--dream-textures"}},"signature":"T53YB6LKAq4n1RNi4SB2HwvJKjbJGL4LBIRY1UrYdzvqCw7JxR15Itth9llDMJgi5iwNyjlrg2ANjkVAb8nvCw==","signedAt":"2026-06-21T22:49:06.563Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/carson-katri--dream-textures","artifact":"https://unfragile.ai/carson-katri--dream-textures","verify":"https://unfragile.ai/api/v1/verify?slug=carson-katri--dream-textures","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}